U.S. patent application number 15/352011 was filed with the patent office on 2017-05-25 for method and apparatus for fuel consumption prediction and cost estimation via crowd-sensing in vehicle navigation system.
The applicant listed for this patent is GM GLOBAL TECHNOLOGY OPERATIONS LLC. Invention is credited to FAN BAI, DAVID E. BOJANOWSKI, DONALD K. GRIMM, OMER TSIMHONI, MICHAEL WAGNER.
Application Number | 20170146362 15/352011 |
Document ID | / |
Family ID | 58693719 |
Filed Date | 2017-05-25 |
United States Patent
Application |
20170146362 |
Kind Code |
A1 |
BAI; FAN ; et al. |
May 25, 2017 |
METHOD AND APPARATUS FOR FUEL CONSUMPTION PREDICTION AND COST
ESTIMATION VIA CROWD-SENSING IN VEHICLE NAVIGATION SYSTEM
Abstract
A system and method for providing navigation routing options to
a vehicle driver, including estimated fuel consumption and fuel
cost. A server collects data from a large number of road vehicles
driving different routes, where the data includes road grade,
average speed, stop/start and acceleration/deceleration info and
vehicle specifications, and the data is collected via a telematics
or other wireless system. The server also receives map data, point
of interest data and real-time traffic data from their respective
providers. When a driver of a road vehicle requests navigation
routing from a start point to a destination, the server provides
multiple routing options including not only distance and time for
each routing option, but also fuel consumption and cost. The
estimated fuel consumption is computed using models based on the
crowd-sensed data from the other vehicles driving the routes, where
the models include a physics-based model and a machine learning
model.
Inventors: |
BAI; FAN; (ANN ARBOR,
MI) ; BOJANOWSKI; DAVID E.; (CLARKSTON, MI) ;
GRIMM; DONALD K.; (UTICA, MI) ; WAGNER; MICHAEL;
(RIMBACH, DE) ; TSIMHONI; OMER; (BLOOMFIELD HILLS,
MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GM GLOBAL TECHNOLOGY OPERATIONS LLC |
DETROIT |
MI |
US |
|
|
Family ID: |
58693719 |
Appl. No.: |
15/352011 |
Filed: |
November 15, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62257551 |
Nov 19, 2015 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G08G 1/0129 20130101;
G08G 1/09685 20130101; G07C 5/008 20130101; G08G 1/0141 20130101;
G08G 1/096816 20130101; G01C 21/3469 20130101; G01C 21/3476
20130101; G06N 3/04 20130101; G01C 21/3492 20130101; G08G 1/0112
20130101; G06F 30/20 20200101; G06F 17/11 20130101; G01C 21/3697
20130101; G06N 3/08 20130101 |
International
Class: |
G01C 21/36 20060101
G01C021/36; G08G 1/01 20060101 G08G001/01; G06F 17/11 20060101
G06F017/11; G06N 3/08 20060101 G06N003/08; G06N 3/04 20060101
G06N003/04; G06F 17/50 20060101 G06F017/50; G07C 5/00 20060101
G07C005/00; G01C 21/34 20060101 G01C021/34 |
Claims
1. A method for providing estimated fuel consumption for a driving
route in a vehicle navigation system, said method comprising:
providing vehicle operational data from a plurality of road
vehicles to a server computer, where the vehicle operational data
is collected by sensors on the road vehicles and provided
wirelessly to the server computer, and the vehicle operational data
includes data characterizing each of the road vehicles driving over
one or more road segments including fuel consumption for each of
the road vehicles for each of the road segments; creating one or
more fuel consumption models on the server computer, where the one
or more fuel consumption models compute fuel consumption for each
of the road vehicles for each of the road segments based on the
vehicle operational data, properties of road segments, and behavior
characteristics of drivers; requesting navigation instructions from
a start point to a destination by an occupant of a host vehicle
using a navigation system in the host vehicle; determining one or
more driving routes from the start point to the destination;
computing an estimated fuel consumption and a fuel cost for the
host vehicle to drive each of the one or more driving routes, using
the one or more fuel consumption models; and displaying the one or
more driving routes, with the fuel consumption and the fuel cost
for each of the one or more driving routes on the navigation
system.
2. The method of claim 1 wherein the vehicle operational data
includes vehicle and engine model, road grade, traffic flow speed,
individual vehicle acceleration/braking patterns, individual
vehicle deviation from average traffic speed, and idling caused by
stop signs, traffic lights and congestion.
3. The method of claim 2 wherein the vehicle operational data also
includes elevation and outside air temperature.
4. The method of claim 1 wherein the one or more fuel consumption
models include a vehicle kinematic physics-based model which
simulates vehicle forces and motions in order to compute fuel
consumption for a road segment.
5. The method of claim 4 wherein the vehicle kinematic
physics-based model includes, for each road segment, a vehicle
dynamic profile which simulates vehicle motion as a function of
time for the road segment, an engine speed profile computed based
on the vehicle dynamic profile, and an engine torque profile
computed based on the vehicle dynamic profile and forces affecting
the vehicle, and estimates fuel consumption for the road segment by
computing a time integral of the engine speed profile and the
engine torque profile.
6. The method of claim 1 wherein the one or more fuel consumption
models include a machine learning model which correlates the
vehicle operational data to fuel consumption for each of the road
segments.
7. The method of claim 6 wherein the machine learning model is a
neural network model, and the neural network model includes a
training mode in which it is calibrated using the vehicle
operational data and the fuel consumption data from the plurality
of road vehicles, and a testing mode in which it predicts fuel
consumption for the host vehicle.
8. The method of claim 6 wherein the machine learning model is a
statistical learning model, and the statistical learning model
includes a training mode in which it is calibrated using the
vehicle operational data and the fuel consumption data from the
plurality of road vehicles, and a testing mode in which it predicts
fuel consumption for the host vehicle.
9. The method of claim 1 wherein computing an estimated fuel
consumption and a fuel cost is performed by the server computer,
and the estimated fuel consumption and the fuel cost are downloaded
to the navigation system in the host vehicle.
10. The method of claim 1 wherein computing an estimated fuel
consumption and a fuel cost is performed by the navigation system
in the host vehicle, using the one or more fuel consumption models
downloaded from the server computer to the navigation system.
11. The method of claim 1 further comprising receiving map data,
point of interest (POI) data and real-time traffic data from
supplemental data providers, by the server computer, incorporating
the map data and the real-time traffic data into the fuel
consumption models, and incorporating the POI data into the driving
route and fuel cost displayed on the navigation system.
12. The method of claim 11 further comprising providing updated
data to the supplemental data providers by the server computer.
13. A method for providing estimated fuel consumption for a driving
route in a vehicle navigation system, said method comprising:
providing vehicle operational data from a plurality of road
vehicles to a server computer, where the vehicle operational data
is collected by sensors on the road vehicles and provided
wirelessly to the server computer, and the vehicle operational data
includes data characterizing each of the road vehicles driving over
one or more road segments including fuel consumption for each of
the road vehicles for each of the road segments, where the vehicle
operational data includes vehicle and engine model, road grade,
traffic flow speed, individual vehicle acceleration/braking
patterns, individual vehicle deviation from average traffic speed,
idling caused by stop signs, traffic lights and congestion,
elevation and outside air temperature; receiving map data, point of
interest (POI) data and real-time traffic data from supplemental
data providers, by the server computer; creating one or more fuel
consumption models on the server computer, where the one or more
fuel consumption models compute fuel consumption for each of the
road vehicles for each of the road segments based on the vehicle
operational data, properties of road segments, behavior
characteristics of drivers and the data from the supplemental data
providers, and where the one or more fuel consumption models are
selected from a group including a vehicle kinematic physics-based
model which simulates vehicle forces and motions in order to
compute fuel consumption for a road segment and a machine learning
model which correlates the vehicle operational data to fuel
consumption for each of the road segments; requesting navigation
instructions from a start point to a destination by an occupant of
a host vehicle using a navigation system in the host vehicle;
determining one or more driving routes from the start point to the
destination; computing an estimated fuel consumption and a fuel
cost for the host vehicle to drive each of the one or more driving
route using the one or more fuel consumption models; and displaying
the one or more driving routes, with the fuel consumption, the fuel
cost and POI data for each of the one or more driving routes on the
navigation system.
14. A system for providing estimated fuel consumption for a driving
route in a vehicle navigation system, said system comprising: a
server computer including a processor and a memory, said server
computer being configured to receive operational data from a
plurality of vehicles being driven over a plurality of road
segments, and further configured to compute one or more fuel
consumption models which calculate fuel consumption for each of the
vehicles driving each of the road segments based on the operational
data, properties of road segments, and behavior characteristics of
drivers; and a navigation system in a host vehicle, said navigation
system being configured to wirelessly communicate with the server
computer, where an occupant of the host vehicle uses the navigation
system to make a request for navigation instructions from a start
point to a destination, one or more navigation routing options are
determined in response to the request, and an estimated fuel
consumption for each of the routing options is calculated using the
one or more fuel consumption models and is provided to the occupant
of the host vehicle.
15. The system of claim 14 wherein the operational data includes
vehicle and engine model, road grade, traffic flow speed,
individual vehicle acceleration/braking patterns, individual
vehicle deviation from average traffic speed, and idling caused by
stop signs, traffic lights and congestion.
16. The system of claim 15 wherein the operational data also
includes elevation and outside air temperature.
17. The system of claim 14 wherein the one or more fuel consumption
models include a vehicle kinematic physics-based model which
simulates vehicle forces and motions in order to compute fuel
consumption for a road segment.
18. The system of claim 17 wherein the vehicle kinematic
physics-based model includes, for each road segment, a vehicle
dynamic profile which simulates vehicle motion as a function of
time for the road segment, an engine speed profile computed based
on the vehicle dynamic profile, and an engine torque profile
computed based on the vehicle dynamic profile and forces affecting
the vehicle, and estimates fuel consumption for the road segment by
computing a time integral of the engine speed profile and the
engine torque profile.
19. The system of claim 14 wherein the one or more fuel consumption
models include a machine learning model which correlates the
operational data to fuel consumption for each of the road
segments.
20. The system of claim 19 wherein the machine learning model is a
neural network model, and the neural network model includes a
training mode in which it is calibrated using the operational data
and the fuel consumption data from the plurality of road vehicles,
and a testing mode in which it predicts fuel consumption for the
host vehicle.
21. The system of claim 19 wherein the machine learning model is a
statistical learning model, and the statistical learning model
includes a training mode in which it is calibrated using the
operational data and the fuel consumption data from the plurality
of road vehicles, and a testing mode in which it predicts fuel
consumption for the host vehicle.
22. The system of claim 14 wherein computing an estimated fuel
consumption and a fuel cost is performed by the server computer,
and the estimated fuel consumption and the fuel cost are downloaded
to the navigation system in the host vehicle.
23. The system of claim 14 wherein computing an estimated fuel
consumption and a fuel cost is performed by the navigation system
in the host vehicle, using the one or more fuel consumption models
downloaded from the server computer to the navigation system.
24. The system of claim 14 further comprising receiving map data,
point of interest (POI) data and real-time traffic data from
supplemental data providers, by the server computer, incorporating
the map data and the real-time traffic data into the fuel
consumption models, and incorporating the POI data into the driving
route and fuel cost displayed on the navigation system.
25. The system of claim 24 further comprising providing updated
data to the supplemental data providers by the server computer.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of the priority date of
U.S. Provisional Patent Application Ser. No. 62/257,551, titled,
Method and Apparatus for Fuel Consumption Prediction and Cost
Estimation Via Crowd-Sensing in Vehicle Navigation System, filed
Nov. 19, 2015.
BACKGROUND OF THE INVENTION
[0002] Field of the Invention
[0003] This invention relates generally to a vehicle navigation
system and, more particularly, to a system and method for providing
navigation routing options to a vehicle driver or autonomous
vehicle navigation system, where the navigation routes offered by
the system each include an estimated fuel consumption and fuel
cost, and where the estimated fuel consumption is computed using
models based on crowd-sensed data from other vehicles driving the
routes.
[0004] Description of the Related Art
[0005] Navigation systems have become increasingly popular with
vehicle drivers in recent years, as the functionality and
reliability of such systems have improved dramatically. Many new
vehicles include a navigation system delivered as original
equipment by the vehicle manufacturer, which is typically
integrated with a telematics system and vehicle audio/visual
systems. Other types of navigation systems include stand-alone
devices which can be purchased and simply placed in a vehicle, and
even smart phones equipped with navigation application software.
With all of these types of navigation systems, route planning,
visual map display and turn-by-turn driver guidance are available.
Collectively, these features have made navigation systems virtually
indispensable to many drivers.
[0006] In any of the navigation systems described above, a driver
can request navigation instructions for driving from a starting
point to a destination. Most navigation systems have the capability
to offer more than one route from the starting point to the
destination. For example, a navigation system might offer three
different routes to the destination, where one route includes only
high-speed-limit roadways but has a greatest distance, a second
route features the shortest distance but involves mostly low-speed
roads and streets, and a third route includes speed limits and
distances which are in between the first two route options.
Existing navigation systems typically estimate the total driving
distance and the amount of time it will take to drive each of the
optional routes. While all of these features are helpful, drivers
and/or autonomous driving systems can benefit from additional
information about navigation routing options which may help the
driver or system determine which route to take.
SUMMARY OF THE INVENTION
[0007] In accordance with the teachings of the present invention, a
system and method are disclosed for providing navigation routing
options to a vehicle driver or autonomous driving system, where the
navigation routes offered by the system each include an estimated
fuel consumption and fuel cost. A server collects data from a large
number of road vehicles driving different routes, where the data
includes road grade, average speed, stop/start and
acceleration/deceleration, elevation (height above sea level), and
outside air temperature and related weather info and vehicle
specifications, and the data is collected via a telematics system
or other wireless transmission. The server also receives map data,
point of interest data and real-time traffic data from their
respective providers. When a driver of a road vehicle requests
navigation routing from a start point to a destination, the server
provides multiple routing options including not only distance and
time for each routing option, but also fuel consumption and cost.
The estimated fuel consumption is computed using models based on
the crowd-sensed data from the other vehicles driving the routes,
where the models include a physics-based model and a machine
learning model.
[0008] Additional features of the present invention will become
apparent from the following description and appended claims, taken
in conjunction with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1 is an illustration of a display from a typical
navigation system, showing three possible routes from a start point
to a destination, with a distance and time associated with each
route;
[0010] FIG. 2 is a block diagram illustration of a system framework
for collecting crowd-sensed vehicle driving data and other data,
and computing estimated fuel consumption for requested navigation
routes;
[0011] FIG. 3 is an illustration of a neural network model used to
estimate fuel consumption based on a number of road topology and
driver behavior parameters;
[0012] FIG. 4 is a flowchart diagram of a method for providing fuel
consumption prediction and fuel cost estimation in a vehicle
navigation system; and
[0013] FIG. 5 is an illustration of a display from a navigation
system according to an embodiment of the disclosed invention,
showing the three navigation routes and data as in FIG. 1, and also
including fuel consumption and cost for each route.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0014] The following discussion of the embodiments of the invention
directed to a system and method for providing fuel consumption
prediction and fuel cost estimation in a vehicle navigation system
is merely exemplary in nature, and is in no way intended to limit
the invention or its applications or uses. For example, the
embodiments discussed below are described in the context of a
driver using an in-vehicle navigation system; however, the
inventive concept of calculating and providing fuel consumption
data for various routing alternatives is equally applicable to
web-based map programs which are accessed via computers or handheld
mobile devices. The concept is also applicable to autonomous
driving systems that perform automatic re-routing functions and
select a route autonomously or based on input preferences from the
vehicle administrator or occupant.
[0015] Navigation systems are well known in the art which provide
route planning, visual map display and turn-by-turn driver
navigation guidance. When a driver requests navigation instructions
for driving from a starting point to a destination, most navigation
systems have the capability to offer more than one route option,
and basic distance and time estimates are provided for each route
option. There are some situations where a navigation system offers
only one route option, as only one routing makes sense, but in many
other situations, more than one route option is offered.
[0016] FIG. 1 is an illustration of a display 10 from a typical
in-vehicle navigation system, such as is known in the art. The
display 10 represents a scenario where a driver has requested
navigation instructions from a start point 20 to a destination 30.
The start point 20 typically defaults to the current location of
the vehicle and driver, but could be anywhere. The display 10 shows
three possible routes from the start point 20 to the destination
30, including a route 40, a route 50 and a route 60. The route 40
entails the longest driving distance of the three routing options,
but may include all highway driving with few or no stops. The route
60 entails the shortest driving distance, but may include low-speed
driving and frequent stops at stop signs and stop lights. The route
50 has an intermediate driving distance (shorter than the route 40
but longer than the route 60), and may include moderate travel
speeds.
[0017] Because of the different characteristics of the routing
options, it is known for navigation systems to provide the driver
with a distance and time associated with each route. A table 70
includes, for each of the routes 40/50/60, a total distance and an
estimated time. Of course, the distance and time may be displayed
to the driver in a form other than the table 70--such as a side
note appended to each of the routes 40/50/60. The time estimate and
distance data may help the driver decide which route to take.
However, using the systems and methods discussed below, more
information can be provided to the driver to help the driver make a
more informed decision as to which route to travel.
[0018] FIG. 2 is a block diagram illustration of a system framework
100 for collecting crowd-sensed vehicle driving data and other
data, and computing estimated fuel consumption for requested
navigation routes. As shown in FIG. 2, a plurality of vehicles 102
are shown driving on a roadway 110. The roadway 110 is divided into
road segments 112, 114, 116, etc., where the road segments are
typically determined by map providers, and each segment is a fairly
short stretch of road having consistent attributes and
characteristics. For example, a road segment in a residential
neighborhood might be one block long, where the road has constant
number of lanes and speed limit for the entire segment. On a
highway, number of lanes, speed limit, entrance/exit ramps, road
grade and road curvature may all be used to determine the extent of
each road segment.
[0019] Although only the roadway 110 is shown in FIG. 2, in an
actual implementation of the framework 100, a large number of the
vehicles 102 would be driving on many different roadways. A vehicle
104 will request navigation instructions, as discussed below.
[0020] The vehicles 102 and 104 wirelessly communicate with a
server 120. The wireless communications between the vehicles
102/104 and the server 120 may use any suitable
technology--including but not limited to, proprietary telematics
systems, cellular communications, satellite communications,
vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I)
communications using dedicated short range communications (DSRC) or
other technology, etc. Further, the communications between the
vehicles 102/104 and the server 120 may pass through various
Internet routes, proprietary communications networks and servers,
and other intermediate routing devices. In the framework 100, it is
simply necessary for the vehicles 102/104 to be able to communicate
in a two-way fashion with the server 120 in real-time or near
real-time.
[0021] Of course, not every vehicle on any road will be in
communication with the server 120, as many vehicles will not be so
equipped. The server 120 will collect data from as many vehicles as
possible for all roadways within its purview and, if more vehicles
are providing data than the server 120 needs to maintain good
quality fuel consumption models, then the server 120 will
selectively receive data from a preferred number of vehicles.
[0022] The server 120 includes computational capability and storage
capability, as would be understood by anyone familiar with
computers. The server 120 is programmed with at least one of two
different types of fuel consumption prediction models--a
physics-based model, and a machine learning model. A data
collection module 122 collects data from the vehicles 102 for a
physics-based model 124, and a data collection module 132 collects
data from the vehicles 102 for a machine learning model 134. The
data collected by the data collection modules 122 and 132 may be
slightly different, and the data collection and the models are
discussed in detail below.
[0023] One or more vehicle-manufacturer-private servers 140 provide
proprietary vehicle model data 142 to the server 120, and may also
receive data back from the server 120. The vehicle model data 142
may be used in the physics-based model 124 and/or the machine
learning model 134 to estimate fuel consumption by the vehicle 104,
as will also be discussed further below.
[0024] External data providers 152/154/156 provide data to the
server 120 for use in the fuel consumption models. The external
data providers 152/154/156 include a point-of-interest (POI) data
provider 152 with fuel station location data being of primary
interest, a map data provider 154 and a real-time traffic data
provider 156. Other types of external data may also be provided, to
improve the fuel consumption prediction (e.g., current weather and
forecasted weather information). Communications between the server
120 and the external data providers 152/154/156 may be two-way in
nature, as the server 120 may collect data from the vehicles 102
which is more accurate and/or more current than data possessed by
the external data providers 152/154/156.
[0025] To summarize the framework 100, the server 120 continuously
collects data from many road vehicles 102 and other data sources
(142, 152-156), and continuously refines fuel consumption
estimation models 124 and 134. When the vehicle 104 requests
navigation routing instructions, the server 120 provides at least
one (and often more than one) routing option, including distance,
estimated time, and estimated fuel consumption and cost for each of
the routing options. Following is a discussion of how the models
124 and 134 operate.
[0026] As the name implies, the physics-based model 124 uses
calculations based on engine performance, vehicle parameters and
vehicle environment to estimate fuel consumption for a particular
vehicle and driver over the road segments of a particular
navigation route. When reduced to first principles, the motion of
the vehicle 104 (or any vehicle which is the subject of the fuel
consumption calculations) is characterized by Newton's second law,
which states that the sum of the forces acting on the vehicle 104
is equal to its mass times its acceleration. When extended to
include other equivalent forces which affect fuel consumption,
Newton's second law equation can be written as:
m v t = F eng - F fric - F grav - F aero - F idle - F elec - F
brake , ( 1 ) ##EQU00001##
where m is the mass of the vehicle 104,
v t ##EQU00002##
is the vehicle acceleration (rate of change of longitudinal
velocity), F.sub.eng is the force provided by the vehicle engine,
F.sub.fric is the road friction force, F.sub.grav is the force of
gravity acting longitudinally on the vehicle (if there is a road
slope), F.sub.aero is the aerodynamic drag force on the vehicle,
F.sub.idle is the equivalent force acting on the vehicle associated
with stops and starts along the roadway, F.sub.elec is the
equivalent force associated with the vehicle electrical system, and
F.sub.brake is the braking force.
[0027] Each of the forces in Equation (1) can be modeled using a
combination of vehicle parameters, engine parameters, roadway and
driver parameters and empirical data. Specifically, the forces may
be modeled as follows:
F eng = k 0 .eta. .gamma. .gamma. final R wheel T , ( 2 )
##EQU00003##
where F.sub.eng is forward force (thrust) on the vehicle due to the
engine, k.sub.0 is a constant, .eta. is engine efficiency, .gamma.
is transmission efficiency, .gamma..sub.final is final drive
efficiency, T is engine torque, and R.sub.wheel is wheel radius.
Using an engine model, a fuel consumption associated with a
particular engine force F.sub.eng can be determined.
[0028] Continuing through the force terms of Equation (1):
F.sub.fric=k.sub.1m cos .alpha.(r.sub.0+r.sub.1v) (3)
where F.sub.fric is friction force on the vehicle from the road,
k.sub.1 is a constant, m is vehicle mass, a is road grade, r.sub.0
is a road friction resistance value, r.sub.1 is a rolling
resistance coefficient, and v is vehicle velocity.
[0029] Continuing through the force terms of Equation (1):
F.sub.grav=k.sub.2 mg sin .alpha., (4)
where F.sub.grav is a longitudinal component of gravity force on
the vehicle due to road grade, k.sub.2 is a constant, m is vehicle
mass, g is acceleration of gravity, and .alpha. is road grade.
[0030] Continuing through the force terms of Equation (1):
F.sub.aero=k.sub.31/2.rho.AC.sub.dv.sup.2, (5)
where F.sub.aero is aerodynamic drag force on the vehicle, k.sub.3
is a constant, .rho. is air density, A is frontal area of the
vehicle, C.sub.d is a drag coefficient based on shape of the
vehicle, and v is vehicle velocity.
[0031] Continuing through the force terms of Equation (1):
F idle = k 4 mv 2 ( ST + TL dist ) , ( 6 ) ##EQU00004##
where F.sub.idle is an equivalent force on the vehicle associated
with idling, k.sub.4 is a constant, m is vehicle mass, v is vehicle
velocity, and ST+TL is number of stop signs and traffic lights
encountered per unit distance dist over the road segment.
[0032] Continuing through the force terms of Equation (1):
F.sub.elec=k.sub.5p.sub.e, (7)
where F.sub.elec is an equivalent force on the vehicle associated
with electrical system energy consumption, k.sub.5 is a constant,
and p.sub.e is an electrical system loss coefficient.
[0033] For the final term of Equation (1), F.sub.brake is braking
force on the vehicle, which may be determined by longitudinal
acceleration or by sensors in a brake system such as an anti-lock
braking system. In vehicles with a regenerative braking system
where some braking torque is captured as kinetic or electrical
energy, this can be accounted for in the model so that the braking
force F.sub.brake is not treated as pure wasted energy.
[0034] Some of the parameters which appear in Equations (1)-(7) may
vary from one driving trip to another for the individual vehicle
104. For example, the vehicle mass m will be significantly
different if the vehicle 104 has only the driver onboard, versus
being fully loaded with 5-6 adult passengers, or loaded with heavy
cargo. Likewise, a pickup truck with bulky cargo in the bed, or
pulling a trailer, will have a higher mass and a higher drag
coefficient than the standard vehicle. Adjustment of these
parameters to the configuration of the current driving trip can be
accomplished by a combination of measurement and learning
techniques--where, for example, vehicle mass may be directly
measurable through load sensors in the suspension, and drag
coefficient may be learned by calculations made while driving on a
level road at a constant speed.
[0035] The physics-based model 124 can be constructed using
Equations (1)-(7), such that a vehicle's fuel consumption can be
calculated for any given road segment. This is done by modeling a
vehicle dynamic profile--accelerations, decelerations, stop, starts
and road grade climbs--over the course of each road segment, using
the crowd-sensed and vehicle/driver-specific data described above.
Once the vehicle dynamic profile is modeled, transmission gear
selection and therefore engine speed can also be predicted for the
drive along the road segment. The engine torque profile (vs. time)
is also available from Equation (1), including torque needed to
move the vehicle and other sources of drag and inefficiency for the
road segment (Equations (3)-(7)). Thus, a time-based simulation of
engine speed and torque over the duration of the road segment drive
is now available. Using the engine torque and speed time profile,
fuel consumption as a function of time can be written as:
f(t)=CW(t)T(t), (8)
where f (t) is fuel consumption as a function of time, C is a
calibration constant, W(t) is engine speed as a function of time,
and T (t) is engine torque as a function of time.
[0036] With fuel consumption modeled as a function of time as shown
in Equation (8), total fuel consumption F.sub.i for a road segment
i can be computed as:
F.sub.i=.intg..sub.i=0.sup.T f(t)dt. (9)
[0037] The constants k in Equations (2)-(7) can be determined to
provide the best correlation to actual fuel consumption data for a
large number of vehicles, where the actual fuel consumption data is
also crowd-sensed from the vehicles 102. Once the physics-based
model 124 is correlated to actual road data for a large number of
the vehicles 102, the model 124 can be used in a predictive manner
to estimate fuel consumption for any particular vehicle 104 driving
over any particular road segment.
[0038] In the past, it was not possible to use Equations (1)-(7) to
accurately estimate fuel consumption in advance for a planned
driving route, because many of the parameters in Equations (2)-(7)
were not available a priori for the calculations. For example, the
road grade a, the traffic flow speed v, individual
acceleration/braking patterns and deviation from average traffic
speed, and idling caused by stop signs and traffic lights would
typically not be known for any arbitrary navigation route which may
be requested. However, using the framework 100 discussed above, all
of this data and more may be crowd-sensed from the vehicles 102 or
determined for the individual vehicle 104, and used in the
physics-based model 124 for fuel consumption estimation.
[0039] FIG. 3 is an illustration of a neural network model 200 used
to estimate fuel consumption based on a number of road topology and
driver behavior parameters. The neural network model 200 is one
type of model which may be used as the machine learning model 134.
Other types of machine learning models may also be used, as would
be understood by those skilled in the art.
[0040] The neural network model 200 uses machine learning
techniques to build a model which relates a number of input
parameters 210 to an output parameter 270. Unlike the physics-based
model 124, the neural network model 200 (or any machine learning
model 134) does not model the physics of vehicle forces and
motions, but rather uses numerical techniques to optimize the
correlation between a set of input parameters and an output
parameter. The input parameters 210 are similar to the inputs used
in the physics-based model 124. In one embodiment, eight of the
input parameters 210 are used, including road grade in the form of
altitude of the end of a road segment relative to altitude of the
start of the segment, total ascent distance (vertical), total
descent distance (vertical), average speed, average absolute
acceleration, number of stops, total stop duration and number of
large accelerations. In another embodiment, actual elevation
(height above sea level; not just relative climb/descent for a road
segment) and outside air temperature are included as inputs. Other
combinations of the input parameters 210 could of course be used.
The intention is that the input parameters 210 include the factors
which are most significant in determining fuel consumption of a
vehicle driving over a road segment.
[0041] It is noted that some of the input parameters 210 are
related to road topology--for example, the altitude, ascent and
descent parameters. Others of the input parameters 210 correlate to
driver behavior--such as acceleration data. Still others of the
input parameters 210 may relate to a combination of road topology,
driver behavior and traffic conditions--such as average speed, and
number of stops. This is much the same as the parameters used as
inputs in the physics-based model 124.
[0042] The input parameters 210 are provided to an input layer of
nodes 220, as shown. An adaptive model core 230 includes, in this
case, two layers of internal nodes--a first internal layer 240 and
a second internal layer 250. The second internal layer 250 connects
with an output layer 260 having a single node, which represents the
output parameter 270--in this case, fuel consumption.
[0043] The neural network model 200 is first operated in a training
mode in which a large number of data sets are provided, where each
of the training data sets includes not only the input parameters
210 but also the output parameter 270 (actual fuel consumption) for
a driven road segment. During the training mode, the model 200
constructs itself to provide the best possible correlation between
the input parameters and the output parameter for each training
set. The number of layers in the adaptive model core 230, the
number of nodes in each of the layers 240 and 250, and the
connectivity between the layers 220, 240, 250 and 260 are all
varied during training mode to achieve the best correlation. A back
propagation channel 280 represents the feedback used for adaptive
model training.
[0044] After a sufficient number of data sets are provided for the
training mode, and the neural network model 200 demonstrates good
correlation of model-computed fuel consumption with actual fuel
consumption, then the model 200 is ready to be used in a testing
mode. In the testing mode, the model 200 is used to predict fuel
consumption for a vehicle (the vehicle 104) which is planning to
drive a particular road segment--or more particularly, a navigation
route made up of many road segments. In the testing mode, the input
parameters 210 are obtained from a combination of crowd-sensing
data, data about the vehicle 104 and its driver, map data and
real-time traffic data. Some parameters about the vehicle 104 or
its driver may need to be adjusted specifically for the current
driving trip, in the manner discussed above. With all of the input
parameters 210 being available from these sources, and the neural
network model 200 having been previously trained, the model 200 can
be used to predictively estimate fuel consumption for a particular
vehicle, driver and road segment.
[0045] Many different machine learning techniques are known in the
art and may be suitable for the machine learning model 134
including the neural network model 200--such as support vector
machine (SVM) regression with a radial basis function (RBF) kernel.
Anomaly (outlier) detection, regularization and cross-validation
are also considered to improve accuracy. Other statistical learning
methods, such as Bayesian network analysis, could be used to
achieve this goal as well.
[0046] FIG. 4 is a flowchart diagram 300 of a method for providing
fuel consumption prediction and fuel cost estimation in a vehicle
navigation system, using the data collection and modeling
techniques described above.
[0047] At box 302, vehicle operational data is collected from a
plurality of vehicles driving on a plurality of road segments. This
crowd-sensed data is collected by a server in "the cloud" (the
server 120)--which may be anywhere on the Internet, or may be a
server which is privately operated by a vehicle manufacturer with a
proprietary telematics system, or any other server with remote data
collection capability. The data collected at the box 302 is
preferably collected wirelessly and continuously from the plurality
of vehicles. The vehicle operational data collected at the box 302
includes all of the input parameters listed previously for the
physics-based model 124 and the machine learning model 134, as well
as the actual fuel consumption for the vehicles 102 driving
different road segments. The actual fuel consumption data is used
to calibrate and correlate the models 124 and 134, as described
above.
[0048] At box 304, supplemental data is provided to the server
computer 120 by a point of interest data provider 152, a map data
provider 154 and a real-time traffic provider 156. The data
collected from the supplemental data providers 152/154/156 at the
box 304 includes: road map data (road segments, including road
surface type, number of lanes, curvature, intersections, etc.);
point of interest (POI) data, including locations of fuel stations,
and hours of operation, prices, fuel type availability, etc. for
the stations; and real-time traffic data, including average speeds
where below posted speed, locations of road construction and
accidents, etc. It is noted that providing the supplemental data at
the box 304 is not mandatory, as the server 120 may already have
map and POI data, and crowd-sensed data from the vehicles 102 can
take the place of real-time traffic data.
[0049] At box 306, fuel consumption models are created on the
server 120 for the road segments. The fuel consumption models take
into account many factors, including road grade, average speed,
traffic congestion, number of stop lights and stop signs, vehicle
and engine type, driver acceleration/braking patterns, etc., as
discussed previously. The fuel consumption models may include the
physics-based model 124, the machine learning model 134, or both.
The models 124/134 estimate fuel consumption for a planned driving
route based on not only the road-specific factors, but also the
vehicle-specific and driver-specific factors--such as vehicle mass,
engine and transmission specifications, acceleration and braking
characteristics, etc. As described above, the fuel consumption
models 124/134 created at the box 306 are first computed and
calibrated using known fuel consumption data for many vehicles
driving many different road segments, then the models 124/134 are
ready to be used in a predictive mode.
[0050] At box 308, a navigation route is requested by a driver. The
request would be made in a typical fashion through the onboard
vehicle navigation system in the vehicle 104 of FIG. 2, where the
driver specifies a start point (often the vehicle current location)
and a destination.
[0051] At box 310, the server 120 computes one or more navigation
route options for the requested navigation route. The route options
typically include two or three different routes including at least
some differences in roads. After determining the navigation route
options, at box 312 the server 120 computes estimated fuel
consumption and fuel cost for each of the routes using the model or
models. The fuel consumption estimate may be based on the
physics-based model 124, or the machine learning model 134, or a
combination of the two. The fuel cost is computed based on fuel
consumption along a route and fuel price at filling stations which
are located along the route.
[0052] At box 314, the navigation route options are provided to the
driver via the in-vehicle navigation system, where the route
options each include not only distance and time, but also fuel
consumption and fuel cost as computed using the models 124/134. The
route options presented to the driver may also include, especially
for long-distance navigation routes, locations of fuel stations
relative to driving range along each route option--so that it is
readily apparent to the driver whether refueling can be
conveniently completed along any chosen route.
[0053] The process described above may also include providing
proprietary data from a vehicle manufacturer to be used in the
model-based fuel consumption prediction. For example, the
proprietary data may include powertrain performance data for a
particular model of vehicle.
[0054] The process described above may further include providing
feedback data from the server computer 120 back to the point of
interest data provider 152, the map data provider 154 and the
real-time traffic provider 156. For example, a driver may be able
to report a price paid at a fuel station, or an actual driven road
segment may be different in some way than indicated in the map
data, or a driver may experience traffic congestion conditions
which are different (better or worse) than those being reported by
the real-time traffic provider 156. Any data which is experienced
by the vehicles 102 and/or 104 and provided to the server 120, and
which is different than what is provided by the supplemental data
providers 152/154/156, is subject to update.
[0055] Finally, it is also conceivable to run the process described
above on a local processor onboard individual vehicles such as the
vehicle 104. This can be done by downloading either the
physics-based fuel consumption model 124 or the machine learning
model 134, or both, to connected vehicles. Model downloads could be
performed on a periodic (push) basis, or on an as-needed (pull)
basis when a navigation route is being requested. Then, when a
driver wants to plan a route, the in-vehicle navigation system
could provide not only the different routings with time and
distance, but also estimated fuel consumption and fuel cost based
on calculations performed locally in the vehicle using the models
124/134.
[0056] As will be well understood by those skilled in the art, the
several and various steps and processes discussed herein to
describe the invention may be referring to operations performed by
the server 120, another computer, a processor or other electronic
calculating device that manipulate and/or transform data using
electrical phenomenon. The vehicles 102 (crowd-sourced data
providers) and 104 (individual vehicle requesting navigation
routing) are understood to have onboard processors and memory for
collecting data and making calculations, and communications systems
for wirelessly communicating with the server 120. Those computers
and electronic devices may employ various volatile and/or
non-volatile memories including non-transitory computer-readable
medium with an executable program stored thereon including various
code or executable instructions able to be performed by the
computer or processor, where the memory and/or computer-readable
medium may include all forms and types of memory and other
computer-readable media.
[0057] FIG. 5 is an illustration of a display 410 from a navigation
system according to an embodiment of the disclosed invention. The
display 410 includes a table 470 showing the three navigation
routes 40/50/60 and distance and time data as in FIG. 1. The table
470 also includes fuel consumption and cost for each route,
computed as described in detail above. It is expected that many
drivers will select the route which will consume the least amount
of fuel or which has the lowest fuel cost, particularly if the
driving times are relatively comparable. As mentioned previously,
the fuel consumption and cost data for each route need not be
displayed in a table; it can be shown in any other suitable format
for viewing by the driver.
[0058] With fuel consumption and navigation route data available, a
navigation system can provide additional useful information to a
driver. For example, on a long distance drive, the navigation
system can estimate when refueling of the vehicle will be required,
and can indicate on the display 410 where fuel stations exist. The
fuel station location data is obtained from the point of interest
data provider 152 discussed previously. The navigation system can
also comprehend the price of fuel at the fuel stations along the
different routes (available from the POI data provider 152), and
factor this into the data provided in the display 470. For example,
the route 40 may travel through a state with a much higher fuel tax
than the route 60. Local supply and demand can also cause large
differences in fuel prices between locations. Thus, the average
price per unit volume of fuel may be 15% higher along the route 40
than along the route 60. The route-specific fuel purchase price can
be multiplied by the estimated fuel consumption to provide very
accurate estimates of fuel cost for each route, which may be the
determining factor for many drivers.
[0059] A navigation system which provides estimated fuel
consumption and fuel cost for different navigation routing options
gives the driver valuable information to use when selecting a
driving route. Selection of driving routes which use less fuel
results in savings for the individual driver, and provides a
societal benefit as well.
[0060] The foregoing discussion discloses and describes merely
exemplary embodiments of the present invention. One skilled in the
art will readily recognize from such discussion and from the
accompanying drawings and claims that various changes,
modifications and variations can be made therein without departing
from the spirit and scope of the invention as defined in the
following claims.
* * * * *